Determining a significant user location for providing location-based services

Abstract

Systems, methods, and program products for providing services to a user by a mobile device based on the user's daily routine of movement. The mobile device determines whether a location cluster indicates a significant location for the user based on one or more hints that indicate an interest of the user in locations in the cluster. The mobile device can perform adaptive clustering to determine a size of area of the significant location based on how multiple locations converge in the location cluster. The mobile device can provide location-based services for calendar items, including predicting a time of arrival at an estimated location of a calendar item. The mobile device can provide various services related to a location of the mobile device or a significant location of the user through an application programming interface (API).

What is claimed is: 
     
       1. A non-transitory machine-readable medium storing instructions which, when executed by one or more processors of a mobile device, cause the one or more processors to perform operations comprising:
 determining, by the one or more processors, at least one location of a mobile device, the at least one location associated with a geographic location; 
 identifying a historical record of acts performed at the at least one location that provides a hint that the at least one location is a location of interest; 
 determining that a frequency of visits for the mobile device associated with the at least one location satisfies a frequency threshold; 
 responsive to determining that the frequency of visits satisfies the frequency threshold, storing an indication identifying the geographic location as a significant location of interest; and 
 retrieving and displaying a label associated with the significant location. 
 
     
     
       2. The non-transitory machine-readable medium of  claim 1 , the operations further comprising:
 receiving a request for predicting a future location of the mobile device, the request specifying a future time; 
 determining, using at least a current time and the future time, a probability for each state in a state model; and 
 based on the probabilities, providing a location associated with a state as a predicted future location in response to the request. 
 
     
     
       3. The non-transitory machine-readable medium of  claim 2 , wherein the future time of the request includes a time window. 
     
     
       4. The non-transitory machine-readable medium of  claim 2 , the operations further comprising:
 filtering the states in the state model based on a distance between the current time and the future time and the distance between a current location and the location of the states in the state model, wherein the request for predicting the future location of the mobile device includes the current location of the mobile device or the mobile device determines the current location in response to the request. 
 
     
     
       5. The non-transitory machine-readable medium of  claim 2 , the operations further comprising:
 determining the probability for each state in the state model using a current time, a future time, and the current location. 
 
     
     
       6. The non-transitory machine-readable medium of  claim 5 , wherein determining the probability for each state in the state model includes determining a transition probability density of the mobile device moving from a state representing the current location to a location corresponding to a state in one or more transitions. 
     
     
       7. The non-transitory machine-readable medium of  claim 1 , the operations further comprising:
 determining the label for the significant location based on analysis of a pattern for a computing device. 
 
     
     
       8. The non-transitory machine-readable medium of  claim 1 , wherein each state corresponds with a location previously visited by the mobile device. 
     
     
       9. The non-transitory machine-readable medium of  claim 1 , wherein a state model includes multiple transitions between the multiple states, each transition from a first state to a second state indicates that the mobile device previously moved from a corresponding first location to a corresponding second location, and each location and transition is associated with one or more timestamps. 
     
     
       10. The non-transitory machine-readable medium of  claim 1 , the operations further comprising:
 storing the significant location in a state model, wherein each state in the state model is associated with an entry probability based on dwell time for the significant location. 
 
     
     
       11. The non-transitory machine-readable medium of  claim 1 , wherein the frequency threshold is a configurable threshold. 
     
     
       12. A data processing system associated with a mobile device, the system comprising:
 a memory device; and 
 one or more processors to execute instructions stored in the memory device, wherein the one or more processors perform operations to:
 determining, by the one or more processors, at least one location of a mobile device, the at least one location associated with a geographic location; 
 identifying a historical record of acts performed at the at least one location that provides a hint that the at least one location is a location of interest; 
 determining that a frequency of visits for the mobile device associated with the at least one location satisfies a frequency threshold; 
 responsive to determining that the frequency of visits satisfies the frequency threshold, 
 storing an indication identifying the geographic location as a significant location of interest; and 
 retrieving and displaying a label associated with the significant location. 
 
 
     
     
       13. The data processing system of  claim 12 , the operations further comprising:
 receiving a request for predicting a future location of the mobile device, the request specifying a future time; 
 determining, using at least a current time and the future time, a probability for each state in the state model; and 
 based on the probabilities, providing a location associated with a state as a predicted future location in response to the request. 
 
     
     
       14. The data processing system of  claim 13 , the operations wherein the request for predicting the future location of the mobile device includes a current location of the mobile device or the data processing system is to determine the current location of the mobile device in response to the request. 
     
     
       15. The data processing system of  claim 12 , wherein the frequency threshold is a configurable threshold. 
     
     
       16. The data processing system of  claim 12 , the operations further comprising:
 determining the label for the significant location based on analysis of a pattern for a computing device. 
 
     
     
       17. The data processing system of  claim 12 , wherein
 storing the significant location in a state model, wherein each state in the state model is associated with an entry probability based on dwell time for the significant location. 
 
     
     
       18. The data processing system of  claim 17 , the one or more processors further to determine the probability for each state in the state model using the current time, the future time, and the current location. 
     
     
       19. A method comprising:
 determining, by one or more processors, at least one location of a mobile device, the at least one location associated with a geographic location; 
 identifying a historical record of acts performed at the at least one location that provides a hint that the at least one location is a location of interest; 
 determining that a frequency of visits for the mobile device associated with the at least one location satisfies a frequency threshold; 
 responsive to determining that the frequency of visits satisfies the frequency threshold, 
 storing an indication identifying the geographic location as a significant location of interest; and 
 retrieving and displaying a label associated with the significant location. 
 
     
     
       20. The method of  claim 19 , wherein the frequency threshold is a configurable threshold.